Background: Chikungunya virus (CHIKV) is an alphavirus in the Semliki Forest complex, and is most closely related to O’Nyong Nyong virus (ONNV). CHIKV and ONNV are mosquito-borne alphaviruses endemic in East Africa that cause acute febrile illness and arthralgia. The objectives of this study were to measure seroprevalence of CHIKV and ONNV in selected health facilities in Western Kenya and link it to demographics and other risk factors.Methods: The study design was cross sectional in selected health facilities. We tested for anti-CHIKV antibodies using In-house Indirect IgG Enzyme Linked Immunosorbent Assay (ELISA) and In-house IgM Capture ELISA and confirmed with Focus Reduction Neutralization Test (FRNT) for specific alphavirus neutralizing antibodies against CHIKV or ONNV. Mean, median and standard deviation were used to summarize the data. Comparisons of means and medians were done using Student’s t test. Prevalence rates were determined using descriptive statistics (e.g. proportions, rates).Results: From the 382 samples that were successfully collected, 114 (29.84%) had anti-CHIKV antibodies by the ELISA test. Of these, 27 (7.1%) had CHIKV-specific neutralizing antibodies and 5 (1.3%) had ONNV-specific neutralizing antibodies. Age was significantly associated with seropositivity (OR=1.03; P=0.015, 95% C.I 1.01-1.06). Males were less likely to be seropositive (OR=0.67; P=0.358, 95% C.I 0.27-1.52). Risk factors associated with seropositivity included collecting firewood (OR=2.73 95% 1.13- 6.41) and walls with holes and cracks (OR=0.23 95% C.I 0.04 -0.86).Conclusions: Both CHIKV and ONNV infections were confirmed in the participants’ more so in women and adults, demonstrating undocumented and ongoing transmission in Western Kenya. In 2011 and 2012 CHIKV and ONNV contributed 8.4% of fevers presented in the three selected health facilities in Western Kenya.

In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some designbased and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.

The study focuses on the imputation for the longitudinal survey data which often has nonignorable nonrespondents. Local linear regression is used to impute the missing values and then the estimation of the time-dependent finite populations means. The asymptotic properties (unbiasedness and consistency) of the proposed estimator are investigated. Comparisons between different parametric and nonparametric estimators are performed based on the bootstrap standard deviation, mean square error and percentage relative bias. A simulation study is carried out to determine the best performing estimator of the time-dependent finite population means. The simulation results show that local linear regression estimator yields good properties.